Methods and kits used in classifying adrenocortical carcinoma

ABSTRACT

The invention encompasses methods and kits used to detect biomarkers that may be used to predict disease outcome in adrenocortical carcinoma patients.

PRIORITY CLAIM

This application claims priority under 35 U.S.C. §119 (e) from U.S.Provisional Application 61/301,826, filed 5 Feb. 2010, entitled MARKERSOF ADRENOCORTICAL CARCINOMA, which is hereby incorporated by referencein its entirety.

FIELD OF THE INVENTION

This invention is related to tests that use biomarkers to predictdisease outcome. More particularly, the invention is related to teststhat use the expression of biomarkers to predict outcome inadrenocortical carcinoma.

BACKGROUND OF THE INVENTION

Adrenocortical carcinoma (ACC) is an aggressive malignancy that forms inthe adrenal cortex. ACC generally occurs in adults with a median age ofdiagnosis at 44 years. ACC is curable when detected early, but becausemost ACC tumors are still capable of functioning, many patients do notpresent with endocrine symptoms. As a result, between 40% and 70% ofpatients have metastatic disease at the time of diagnosis and cannot becured by surgery. Further, mitotane, the only drug compound approved fortreatment of ACC, has severe, dose limiting side effects and only showsa response in approximately 22% of patients. The combination of latediagnosis and ineffective treatments for those with disseminated diseaseresults in an overall 5-year survival rate of 10-20%.

Factors that predict the outcome of a patient's disease are a form ofpersonalized medicine that can inform health care providers aboutindividual patient treatment. Patients likely to have a poorer outcomemay have the option of undertaking an aggressive treatment regimenincluding chemotherapy or experimental treatments. Efforts have beenmade to seek prognostic factors such as hypo- and hyperdiploidy andexpression profiling. While these may be used to manage treatment of ACCpatients, they are not a single gene prognostic test. A single geneprognostic test could be more readily adopted by health care providers.

SUMMARY OF THE INVENTION

The invention encompasses a method of predicting the disease outcome ofan adrenocortical carcinoma patient on the basis of detecting theexpression of a biomarker such as PTTG1 (nucleic acid sequence SEQ IDNO. 1, protein sequence SEQ ID NO. 2). One such disease outcome may belikelihood of survival past a certain date.

In one embodiment of the invention, the patient is classified into acohort on the basis of protein or DNA expression of PTTG1. In thisembodiment, a first reagent capable of binding to either PTTG1 nucleicacid (such as RNA) or protein is added to a mixture that comprises asample from the patient. The mixture is subjected to conditions thatallow detection of the binding of the reagent to the biomarker. Thesubject may be classified into cohort on the basis of the binding of thereagent to the sample. The cohort may be any cohort, including one ormore individuals not likely to survive past a point in time.

In one aspect of this embodiment, the biomarker is PTTG1 protein. Inthis aspect, the reagent may be an antibody that binds to PTTG1 protein.The antibody may further comprise a label. The label may be any label,including but not limited to a fluorescent compound, an enzyme, or aligand (such as biotin or streptavidin). Additionally, a second antibodywith specificity to the first antibody may be added to the mixture.

In another aspect of this embodiment, the biomarker is a nucleic acidthat encodes PTTG1. In this aspect, the reagent may be a nucleic acidcomplementary to all or part of the marker. The reagent may be anynucleic acid in any form, such as an oligonucleotide. The method mayinvolve adding a second oligonucleotide that binds to part of the markerto the mixture. The first and the second oligonucleotide are constructedto each bind to different parts of the mixture and they each bind todifferent nucleic acid strands. For example, the first oligonucleotidemay bind to the 5′→3′ strand while the second oligonucleotide may bindto the 3′→5′ strand. The mixture is subjected to nucleic acidamplification, such as PCR. Additionally, a third oligonucleotide may beadded to the mixture. The third oligonucleotide is capable of binding toa sequence between the sequences to which the first and secondoligonucleotides are capable of binding. The third oligonucleotide mayhave a label such as a fluorescent label and/or a quencher for theperformance of quantitative PCR. The result may be any result thatsignifies binding such as a band of a certain size visualized throughgel electrophoresis or a Ct value. In addition, DNA sequencing may beperformed on a product of the nucleic acid amplification.

In another aspect of the invention, the first reagent is affixed to asubstrate. In this aspect, a second reagent may be affixed to thesubstrate and the two reagents are configured to form a microarray.

The sample may be any sample, such as a tumor sample.

The patient may have a tumor belonging to a first tumor cluster.

In another embodiment of the invention, a kit is provided that may beused to classify adrenocortical carcinoma tumors. The kit comprises areagent capable of binding to either PTTG1 protein or nucleic acid andan indication of a level of expression that signifies the classificationof the subject into a cohort, such as a cohort of individuals unlikelyto survive past a point in time.

In one aspect of this embodiment, the first reagent comprises a firstantibody. The first antibody may further comprise a label. The label maybe any label. The kit may comprise a second antibody capable of bindingto the first antibody.

In another aspect of this embodiment, the first reagent comprises anucleic acid. The nucleic acid may be conjugated to a substrate. Thenucleic acid may comprise an oligonucleotide. The kit may furthercomprise a second oligonucleotide and the first and secondoligonucleotides are capable of binding to different parts of the markerand to different nucleic acid strands, such as to perform PCR.

In another aspect of this embodiment, the kit may comprise an enzyme.The enzyme may be any enzyme including, for example: a reversetranscriptase, a DNA polymerase, or a thermostable DNA polymerase.

The indication may be any indication of a result that would be used toclassify a patient into a cohort such as a positive control or a printedresult. The printed result may be any printed result and may include butneed not be limited to one or more of the following features eitheralone or in combination: a Ct value, a range of Ct values, an absorbancevalue, a fluorescence value, or a photograph. The printed result may bephysically included in or on the kit or made available via a website.The indication may also comprise software configured to detect bindingof the reagent as input and classification of the subject into thecohort as the output.

It is an object of the invention to provide a test that predicts thedisease outcome for adrenocortical carcinoma patients.

It is an object of the invention to provide a test that allows stagingof treatment for adrenocortical carcinoma patients.

It is an object of the invention to provide a test that preventspatients from undertaking unnecessary treatments that cause grave sideeffects.

DESCRIPTION OF THE FIGURES

A more complete understanding of the present invention may be derived byreferring to the detailed description when considered in connection withthe following illustrative figures.

FIG. 1 depicts a scatter plot showing the association of survival withPTTG1 expression. The Pearson's correlation of −0.696 was significantwith p=0.012.

FIG. 2 depicts a survival curves between tumors with high (>8.5 on theAffymetrix array) or low (<8.5 on the Affymetrix array) PTTG1expression. The cut-off of 8.5 represents the median expression valuefor PTTG1 across all samples.

FIG. 3 depicts the level of expression of PTTG1 in data from Giordano TJ et al, Clin Cancer Res 15, 668-676 (2009) relative to survival.

FIG. 4 depicts data from Giordano T J et al, Clin Cancer Res 15, 668-676(2009) displaying different survival clusters reported in thatreference.

FIG. 5 depicts a significant difference between the expression of PTTG1in ACC tumor cluster 1 from FIG. 4 (low survival) the expression ofPTTG1 in ACC tumor cluster 2 (better survival.)

FIG. 6 depicts the effect of vorinostat on the ACC cell line SW-13.Vorinostat reduces PTTG1 expression in SW13 cells.

FIG. 7 depicts the effect of vorinostat on the ACC cell line H259R.Vorinostat reduces PTTG1 expression in H259R cells.

Elements and acts in the figures are illustrated for simplicity and havenot necessarily been rendered according to any particular sequence orembodiment.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, and for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various aspects of the invention. It will beunderstood, however, by those skilled in the relevant arts, that thepresent invention may be practiced without these specific details. Inother instances, known structures and devices are shown or discussedmore generally in order to avoid obscuring the invention. Aspects andapplications of the invention presented here are described below in thedrawings and detailed description of the invention. Unless specificallynoted, it is intended that the words and phrases in the specificationand the claims be given their plain, ordinary, and accustomed meaning tothose of ordinary skill in the applicable arts.

The invention encompasses methods useful in predicting disease outcomein patients with adrenocortical carcinoma. The methods involve measuringthe expression of one or more biomarkers expressed by adrenocorticalcarcinoma tumors. One such biomarker is any expression product of thehuman PTTG1 gene, whether nucleic acid (such as DNA or RNA) or protein.The expression of the biomarker may be measured or assessed by anymethod including any method of measuring RNA or protein. A result of theexpression of the biomarker may then be used to predict the outcome ofthe disease. The outcome may be any outcome, such as survival of thepatient past a point in time. The invention also encompasses kits orassemblages of components that may be used to practice the methods. Thealso kits include an indication of a result that signifies the outcome.

A biomarker may be any molecular structure produced by a cell, expressedinside the cell, accessible on the cell surface, or secreted by thecell. A biomarker may be any protein, carbohydrate, fat, nucleic acid,catalytic site, or any combination of these such as an enzyme,glycoprotein, cell membrane, virus, cell, organ, organelle, or any uni-or multimolecular structure or any other such structure now known or yetto be disclosed whether alone or in combination. A biomarker may also becalled a marker and the terms are used interchangeably.

A biomarker may be signified by the sequence of a nucleic acid fromwhich it can be derived, including its DNA, RNA, or protein sequence, orany other sequence, chemical, or structural representation. Examples ofnucleic acids that may be biomarkers include miRNA, tRNA, rRNA, siRNA,mRNA, cDNA, or genomic DNA sequences. A biomarker signified by a nucleicacid sequence (such as the sequence of the 5′→3′ strand) also includesthe complementary sequence (such as the sequence of the 3′→5′ strand). Abiomarker is not limited to the products of the exact nucleic acidsequence or protein sequence by which it may be represented.

Examples of molecules encompassed by a biomarker represented by aparticular sequence or structure include point mutations, silentmutations, deletions, frameshift mutations, translocations, alternativesplicing derivatives, differentially methylated sequences,differentially modified protein sequences, truncations, soluble forms ofcell membrane associated biomarkers, and any other variation thatresults in a product that may be identified as the biomarker. Thefollowing nonlimiting examples are included for the purposes ofclarifying this concept: If expression of a specific biomarker in asample is assessed by RTPCR, and if the sample expresses an mRNAsequence different from the sequence used to identify the specificbiomarker by one or more nucleotides, but the biomarker may still bedetected using RTPCR, then the specific biomarker encompasses thesequence present in the sample. Alternatively if expression of aspecific biomarker in a sample is assessed by an antibody and the aminoacid sequence of the biomarker in the sample differs from a sequenceused to identify biomarker by one or more amino acids, but the antibodyis still able to bind to the version of the biomarker in the sample,then the specific biomarker encompasses the sequence present in thesample.

A reagent may be any molecule that is capable of specific binding to abiomarker. Reagents thus include but need not be limited to specificnucleic acids, proteins, fats, natural ligands, small molecules, or anyother molecule disclosed herein or elsewhere that binds specifically orselectively to a particular biomarker. A reagent may also be acombination or mixture of molecules that binds to one or morebiomarkers, such as an antiserum. A reagent may also encompass a set ofreagents that specifically binds a set of biomarkers to constitute abiomarker signature.

Expression of a biomarker encompasses any and all processes throughwhich material derived from a nucleic acid template may be produced.Expression thus includes processes such as RNA transcription, mRNAsplicing, protein translation, protein folding, post-translationalmodification, membrane transport, associations with other molecules,addition of carbohydrate moieties to proteins, phosphorylation, proteincomplex formation and any other process along a continuum that resultsin biological material derived from genetic material whether in vitro,in vivo, or ex vivo. Expression also encompasses all processes throughwhich the production of material derived from a nucleic acid templatemay be actively or passively suppressed. Such processes include allaspects of transcriptional and translational regulation. Examplesinclude heterochromatic silencing, transcription factor inhibition, anyform of RNAi silencing, microRNA silencing, alternative splicing,protease digestion, posttranslational modification, and alternativeprotein folding.

Methods used to assess expression of a biomarker include the use ofnatural or artificial ligands capable of specifically binding abiomarker, particularly a protein biomarker. Such ligands includeantibodies, antibody complexes, conjugates, natural ligands, smallmolecules, nanoparticles, or any other molecular entity capable ofspecific binding to a biomarker. Antibodies may be monoclonal,polyclonal, or any antibody fragment including an Fab, F(ab)₂, Fv, scFv,phage display antibody, peptibody, multispecific ligand, or any otherreagent with specific binding to a biomarker. A ligand may be associatedwith a label such as a radioactive isotope or chelate thereof, a dye(fluorescent or nonfluorescent,) a stain, an enzyme, a metal, or anyother substance capable of aiding a detector in differentiating amixture in which the biomarker is present from a mixture in which thebiomarker is absent.

Methods of assessing the expression of protein biomarkers include flowcytometry, immunohistochemistry, ELISA, Western blot, and immunoaffinitychromatograpy, HPLC, mass spectrometry, protein microarray analysis,PAGE analysis, isoelectric focusing, 2-D gel electrophoresis, or anyenzymatic assay. One skilled in the art would understand whether or nota method would require or be enhanced by the use of a specific ligand.

Expression may be assessed by techniques involving nucleic acidamplification. In general, nucleic acid amplification is a process bywhich copies of a nucleic acid may be made from a source nucleic acid.In some nucleic amplification methods, the copies are generatedexponentially. Examples of nucleic acid amplification include but arenot limited to: the polymerase chain reaction (PCR), ligase chainreaction (LCR,) self-sustained sequence replication (3SR), nucleic acidsequence based amplification (NASBA,) strand displacement amplification(SDA,) amplification with Qβ replicase, whole genome amplification withenzymes such as φ29, whole genome PCR, in vitro transcription withKlenow or any other RNA polymerase, or any other method by which copiesof a desired sequence are generated.

Polymerase chain reaction (PCR) is a particular method of amplifyingDNA, generally involving the making of a reaction mixture by mixing anucleic sample, two or more primers, a DNA polymerase, which may be athermostable DNA polymerase such as Taq or Pfu, and deoxyribosenucleoside triphosphates (dNTP's). In general, the reaction mixture issubjected to temperature cycles comprising a denaturation stage(typically 80-100° C.) an annealing stage with a temperature that maybased on the melting temperature (Tm) of the primers and the degeneracyof the primers, and an extension stage (for example 40-75° C.) Theannealing and extension stages may be combined into a single stage.Other stages may also be used depending on the exact PCR method.

Quantitative PCR incorporates a detectable reporter into the reactionmixture in order to quantify the amount of template amplification. Thedetectable reporter may be, for example, a fluorescent label. The signalfrom the reporter may be detectable upon incorporation into theamplified DNA as is the case with the SYBR Green molecule.Alternatively, the detectable reporter may be linked to anoligonucleotide probe such as in the case of TaqMan™ quantitative PCR.

The oligonucleotide probe may also comprise a quencher molecule. Thequencher hides from detection the majority of the fluorescence that maybe emitted by the fluorescent label when the oligonucleotide probe is insolution. PCR amplification removes the quencher from the probe,rendering the fluorescent molecule detectable. Therefore the quantity orintensity of the fluorescence may be correlated with the amount ofproduct formed in the reaction. One skilled in the art would be capableof calculating the amount of target nucleic acid (either DNA or RNA)present in the original reaction mixture from the quantity of the changein fluorescence. Examples of fluorescent labels that may be used inquantitative PCR include but need not be limited to: HEX, TET, 6-FAM,JOE, Cy3, Cy5, ROX, TAMRA, and Texas Red. An oligonucleotide probe usedin quantitative PCR may also comprise a quencher. Examples of quenchersthat may be used in quantitative PCR include, but need not be limited toTAMRA (which may be used with any of a number of fluorescent labels suchas HEX, TET, or 6-FAM), BHQ1, BHQ2, or DABCYL.

An oligonucleotide probe may include any label. A label may be anysubstance capable of aiding a machine, detector, sensor, device, orenhanced or unenhanced human eye from differentiating a labeledcomposition from an unlabeled composition. Examples of labels includebut are not limited to: a radioactive isotope or chelate thereof, dye(fluorescent or nonfluorescent,) stain, enzyme, or nonradioactive metal.Specific examples include but are not limited to: fluorescein, biotin,digoxigenin, alkaline phosphatese, biotin, streptavidin, ³H, ¹⁴C, ³²P,³⁵S, or any other compound capable of emitting radiation, rhodamine,4-(4′-dimethylamino-phenylazo)benzoic acid (“Dabcyl”);4-(4′-dimethylamino-phenylazo)sulfonic acid (sulfonyl chloride)(“Dabsyl”); 5-((2-aminoethyl)-amino)-naphtalene-1-sulfonic acid(“EDANS”); Psoralene derivatives, haptens, cyanines, acridines,fluorescent rhodol derivatives, cholesterol derivatives;ethylenediaminetetraaceticacid (“EDTA”) and derivatives thereof or anyother compound that may be differentially detected.

When a nucleic acid such as a primer, oligonucleotide, oligonucleotideprobe or any nucleic acid sequence includes a particular sequence, thesequence may be a part of a longer nucleic acid or may be the entiretyof the sequence. The nucleic acid that includes the sequence may containnucleotides 5′ of the sequence, 3′ of the sequence, or both. The conceptof a nucleic acid including a particular sequence further encompassesnucleic acids that contain less than the full sequence that are stillcapable of specifically hybridizing to the target sequence under anyconditions to which a mixture comprising a nucleic acid may besubjected.

A nucleic acid may be identified by the IUPAC letter code which is asfollows: A—Adenine base; C—Cytosine base; G—guanine base; T or U—thymineor uracil base. M—A or C; R—A or G; W—A or T; S—C or G; Y—C or T; K—G orT; V—A or C or G; H—A or C or T; D—A or G or T; B—C or G or T; N or X—Aor C or G or T. Note that T or U may be used interchangeably dependingon whether the nucleic acid is DNA or RNA. A sequence having less than60% 70%, 80%, 90%, 95%, 99% or 100% identity to the identifying sequencemay still be encompassed by the invention if it is able of binding toits complimentary sequence and/or priming nucleic acid amplification ofa desired target sequence. If a sequence is represented in degenerateform; for example through the use of codes other than A, C, G, T, or U;the concept of a nucleic acid including the sequence also encompasses amixture of nucleic acids of different sequences that still meet theconditions imposed by the degenerate sequence.

Reverse transcription PCR is a method of assessing and quantifying RNAexpression, particularly mRNA expression, through nucleic acidamplification. Reverse transcription PCR usually involves a reversetranscription process by which a cDNA template is generated from an RNAsuch as an mRNA. This process is usually accomplished through the use ofa reverse transcriptase enzyme.

Other methods of detecting expression of nucleic acid biomarkers includemicroarray analysis, RNA in situ hybridization, RNAse protection assay,Northern blot, reverse transcriptase treatment followed by direct DNAsequencing, or any other method of detecting a specific nucleic acid nowknown or yet to be disclosed.

Differential expression encompasses any detectable difference betweenthe expression of a biomarker in one sample relative to the expressionof the biomarker in another sample. Differential expression may beassessed in part by a detector. A detector may be any instrument or partof an instrument configured to sense changes in a physical state of asystem, including the aided or unaided human eye. Examples of physicalstates of a system that may change and be sensed by a detector includebut need not be limited to fluorescence, absorbance, radiation, sound,color, light, or any other detectable physical property.

Examples of the use of a detector to assess differential expressioninclude but are not limited to: differential staining of cells in an IHCassay configured to detect a biomarker, differential detection of boundRNA on a microarray to which a sequence capable of binding to thebiomarker is bound, differential results in measuring RTPCR measured inΔCt or alternatively in the number of PCR cycles necessary to reach aparticular optical density at a wavelength at which a double strandedDNA binding dye (e.g. SYBR Green) incorporates, differential results inmeasuring signal from a labeled probe used in a real-time RTPCRreaction, differential detection of fluorescence on cells using a flowcytometer, differential intensities of bands in a Northern blot,differential intensities of bands in an RNAse protection assay,differential cell death measured by apoptotic biomarkers, differentialcell death measured by shrinkage of a tumor, or any method that allows adetection of a difference in signal between one sample or set of samplesand another sample or set of samples.

The expression of the biomarker in a sample may be compared to a levelof expression predetermined to predict the presence or absence of aparticular physiological characteristic. The level of expression may bederived from a single control or a set of controls. A control may be anysample with a previously determined level of expression. A control maycomprise material within the sample or material from sources other thanthe sample. Alternatively, the expression of a biomarker in a sample maybe compared to a control that has a level of expression predetermined tosignal or not signal a cellular or physiological characteristic. Thislevel of expression may be derived from a single source of materialincluding the sample itself or from a set of sources. Comparison of theexpression of the biomarker in the sample to a particular level ofexpression results in a prediction that the sample exhibits or does notexhibit the cellular or physiological characteristic.

Prediction of a cellular or physiological characteristic includes theprediction of any cellular or physiological state that may be predictedby assessing the expression of a biomarker. Examples include theidentity of a cell as a particular cell including a particular normal orcancer cell type, the likelihood that one or more diseases is present orabsent, the likelihood that a present disease will progress, remainunchanged, or regress, the likelihood that a disease will respond or notrespond to a particular therapy, or any other disease outcome. Furtherexamples include the likelihood that a cell will move, senesce,apoptose, differentiate, metastasize, or change from any state to anyother state or maintain its current state.

Expression of a biomarker in a sample may be more or less than that of alevel predetermined to predict the presence or absence of a cellular orphysiological characteristic. The expression of the biomarker in thesample may be more than 1,000,000×, 100,000×, 10,000×, 1000×, 100×, 10×,5×, 2×, 1×, 0.5×, 0.1× 0.01×, 0.001×, 0.0001×, 0.00001×, 0.000001×,0.0000001× or less than that of a level predetermined to predict thepresence or absence of a cellular or physiological characteristic.

One type of cellular or physiological characteristic is the risk that aparticular disease outcome will occur. Assessing this risk includes theperforming of any type of test, assay, examination, result, readout, orinterpretation that correlates with an increased or decreasedprobability that an individual has had, currently has, or will develop aparticular disease, disorder, symptom, syndrome, or any conditionrelated to health or bodily state or if the person currently has thedisease, whether the disease will progress, regress, or remain the same.Examples of disease outcomes include, but need not be limited tosurvival, death, progression of existing disease, remission of existingdisease, initiation of onset of a disease in an otherwise disease-freesubject, or the continued lack of disease in a subject in which therehas been a remission of disease. Assessing the risk of a particulardisease encompasses diagnosis in which the type of disease afflicting asubject is determined. Assessing the risk of a disease outcome alsoencompasses the concept of prognosis. A prognosis may be any assessmentof the risk of disease outcome in an individual in which a particulardisease has been diagnosed. Assessing the risk further encompassesprediction of therapeutic response in which a treatment regimen ischosen based on the assessment. Assessing the risk also encompasses aprediction of overall survival after diagnosis.

Determining the level of expression that signifies a physiological orcellular characteristic may be assessed by any of a number of methods.The skilled artisan will understand that numerous methods may be used toselect a level of expression for a particular biomarker or a pluralityof biomarkers that signifies a particular physiological or cellularcharacteristic. In diagnosing the presence of a disease, a thresholdvalue may be obtained by performing the assay method on samples obtainedfrom a population of patients having a certain type of disease (cancerfor example,) and from a second population of subjects that do not havethe disease. In assessing disease outcome or the effect of treatment, apopulation of patients, all of which have, a disease such as cancer, maybe followed for a period of time. After the period of time expires, thepopulation may be divided into two or more groups. For example, thepopulation may be divided into a first group of patients whose diseaseprogresses to a particular endpoint and a second group of patients whosedisease does not progress to the particular endpoint. Examples ofendpoints include disease recurrence, death, metastasis or other statesto which disease may progress. If expression of the biomarker in asample is more similar to the predetermined expression of the biomarkerin one group relative to the other group, the sample may be assigned arisk of having the same outcome as the patient group to which it is moresimilar.

In addition, one or more levels of expression of the biomarker may beselected that provide an acceptable ability of its ability to signify aparticular physiological or cellular characteristic. Examples of suchcharacteristics include identifying or diagnosing a particular disease,assessing a risk of outcome or a prognostic risk, or assessing the riskthat a particular treatment will or will not be effective.

For example, Receiver Operating Characteristic curves, or “ROC” curves,may be calculated by plotting the value of a variable versus itsrelative frequency in two populations. For any particular biomarker, adistribution of biomarker expression levels for subjects with andwithout a disease may overlap. This indicates that the test does notabsolutely distinguish between the two populations with completeaccuracy. The area of overlap indicates where the test cannotdistinguish the two groups. A threshold is selected. Expression of thebiomarker in the sample above the threshold indicates the sample issimilar to one group and expression of the biomarker below the thresholdindicates the sample is similar to the other group. The area under theROC curve is a measure of the probability that the expression correctlyindicated the similarity of the sample to the proper group. See, e.g.,Hanley et al., Radiology 143: 29-36 (1982) hereby incorporated byreference.

Additionally, levels of expression may be established by assessing theexpression of a biomarker in a sample from one patient, assessing theexpression of additional samples from the same patient obtained later intime, and comparing the expression of the biomarker from the latersamples with the initial sample or samples. This method may be used inthe case of biomarkers that indicate, for example, progression orworsening of disease or lack of efficacy of a treatment regimen orremission of a disease or efficacy of a treatment regimen.

Other methods may be used to assess how accurately the expression of abiomarker signifies a particular physiological or cellularcharacteristic. Such methods include a positive likelihood ratio,negative likelihood ratio, odds ratio, and/or hazard ratio. In the caseof a likelihood ratio, the likelihood that the expression of thebiomarker would be found in a sample with a particular cellular orphysiological characteristic is compared with the likelihood that theexpression of the biomarker would be found in a sample lacking theparticular cellular or physiological characteristic.

An odds ratio measures effect size and describes the amount ofassociation or non-independence between two groups. An odds ratio is theratio of the odds of a biomarker being expressed in one set of samplesversus the odds of the biomarker being expressed in the other set ofsamples. An odds ratio of 1 indicates that the event or condition isequally likely to occur in both groups. An odds ratio grater or lessthan 1 indicates that expression of the biomarker is more likely tooccur in one group or the other depending on how the odds ratiocalculation was set up.

A hazard ratio may be calculated by estimate of relative risk. Relativerisk is the chance that a particular event will take place. It is aratio of the probability that an event such as development orprogression of a disease will occur in samples that exceed a thresholdlevel of expression of a biomarker over the probability that the eventwill occur in samples that do not exceed a threshold level of expressionof a biomarker. Alternatively, a hazard ratio may be calculated by thelimit of the number of events per unit time divided by the number atrisk as the time interval decreases. In the case of a hazard ratio, avalue of 1 indicates that the relative risk is equal in both the firstand second groups. A value greater or less than 1 indicates that therisk is greater in one group or another, depending on the inputs intothe calculation.

Additionally, multiple threshold levels of expression may be determined.This can be the case in so-called “tertile,” “quartile,” or “quintile”analyses. In these methods, multiple groups can be considered togetheras a single population, and are divided into 3 or more bins having equalnumbers of individuals. The boundary between two of these “bins” may beconsidered threshold levels of expression indicating a particular levelof risk of a disease developing or signifying a physiological orcellular state. A risk may be assigned based on which “bin” a testsubject falls into.

A subject includes any human or non-human mammal, including for example:a primate, cow, horse, pig, sheep, goat, dog, cat, or rodent, capable ofdeveloping cancer including human patients that are suspected of havingcancer, that have been diagnosed with cancer, or that have a familyhistory of cancer. Methods of identifying subjects suspected of havingcancer include but are not limited to: physical examination, familymedical history, subject medical history, endometrial biopsy, or anumber of imaging technologies such as ultrasonography, computedtomography, magnetic resonance imaging, magnetic resonance spectroscopy,or positron emission tomography.

The invention contemplates assessing the expression of the biomarker inany biological sample from a subject in which the expression of thebiomarker may be assessed. One skilled in the art would know to select aparticular biological sample and how to collect said sample dependingupon the biomarker that is being assessed. Examples of sources ofsamples include but are not limited to biopsy or other in vivo or exvivo analysis of prostate, breast, skin, muscle, facia, brain,endometrium, lung, head and neck, pancreas, small intestine, blood,liver, testes, ovaries, colon, skin, stomach, esophagus, spleen, lymphnode, bone marrow, kidney, placenta, or fetus. In some aspects of theinvention, the sample comprises a fluid sample, such as peripheralblood, lymph fluid, ascites, serous fluid, pleural effusion, sputum,cerebrospinal fluid, amniotic fluid, lacrimal fluid, stool, or urine.Samples include single cells, whole organs or any fraction of a wholeorgan, in any condition including in vitro, ex vivo, in vivo,post-mortem, fresh, fixed, or frozen.

In some aspects of the invention, the sample comprises cancer cells.Cancer cells include any cells derived from a tumor, neoplasm, cancer,precancer, cell line, malignancy, or any other source of cells that havethe potential to expand and grow to an unlimited degree. Cancer cellsmay be derived from naturally occurring sources or may be artificiallycreated. Cancer cells may also be capable of invasion into other tissuesand metastasis. Cancer cells further encompass any malignant cells thathave invaded other tissues and/or metastasized. One or more cancer cellsin the context of an organism may also be called a cancer, tumor,neoplasm, growth, malignancy, or any other term used in the art todescribe cells in a cancerous state.

Examples of cancers that could serve as sources of cancer cells includesolid tumors such as fibrosarcoma, myxosarcoma, liposarcoma,chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma,endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma,synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma,rhabdomyosarcoma, colon cancer, colorectal cancer, kidney cancer,pancreatic cancer, bone cancer, breast cancer, ovarian cancer, prostatecancer, esophageal cancer, stomach cancer, oral cancer, nasal cancer,throat cancer, squamous cell carcinoma, basal cell carcinoma,adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma,papillary carcinoma, papillary adenocarcinomas, cystadenocarcinoma,medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma,hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonalcarcinoma, Wilms' tumor, cervical cancer, uterine cancer, testicularcancer, small cell lung carcinoma, bladder carcinoma, lung cancer,epithelial carcinoma, glioma, glioblastoma multiforme, astrocytoma,medulloblastoma, craniopharyngioma, ependymoma, pinealoma,hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, skincancer, melanoma, neuroblastoma, and retinoblastoma.

Additional cancers that may serve as sources of cancer cells includeblood borne cancers such as acute lymphoblastic leukemia (“ALL,”), acutelymphoblastic B-cell leukemia, acute lymphoblastic T-cell leukemia,acute myeloblastic leukemia (“AML”), acute promyelocytic leukemia(“APL”), acute monoblastic leukemia, acute erythroleukemic leukemia,acute megakaryoblastic leukemia, acute myelomonocytic leukemia, acutenonlymphocyctic leukemia, acute undifferentiated leukemia, chronicmyelocytic leukemia (“CML”), chronic lymphocytic leukemia (“CLL”), hairycell leukemia, multiple myeloma, lymphoblastic leukemia, myelogenousleukemia, lymphocytic leukemia, myelocytic leukemia, Hodgkin's disease,non-Hodgkin's Lymphoma, Waldenstrom's macroglobulinemia, Heavy chaindisease, and Polycythemia vera.

The present invention further provides kits to be used in assessing theexpression of a biomarker in a subject to predict disease outcome. Kitsinclude any combination of components that facilitates the performanceof an assay. A kit that facilitates assessing the expression of an RNAmay include suitable nucleic acid-based and immunological reagents aswell as suitable buffers, control reagents, and printed protocols.

Kits that facilitate nucleic acid based methods may further include oneor more of the following: specific nucleic acids such asoligonucleotides, labeling reagents, enzymes including PCR amplificationreagents such as Taq or Pfu, reverse transcriptase, or one or more otherpolymerases, and/or reagents that facilitate hybridization. Specificnucleic acids may include nucleic acids, polynucleotides,oligonucleotides (DNA, or RNA), or any combination of molecules thatincludes one or more of the above, or any other molecular entity capableof specific binding to a nucleic acid biomarker. In one aspect of theinvention, the specific nucleic acid comprises one or moreoligonucleotides capable of hybridizing to the biomarker.

An oligonucleotide may be any polynucleotide of at least 2 nucleotides.Oligonucleotides may be less than 10, less than 15, less than 20, lessthan 30, less than 40, less than 50, less than 75, less than 100, lessthan 200, more than 200 or more than 500 nucleotides in length. Whileoligonucleotides are often linear, they may, depending on their sequenceand conditions, assume a two- or three-dimensional structure.Oligonucleotides may be chemically synthesized by any of a number ofmethods including sequential synthesis, solid phase synthesis, or anyother synthesis method now known or yet to be disclosed. Alternatively,oligonucleotides may be produced by recombinant DNA based methods. Oneskilled in the art would understand the length of oligonucleotidenecessary to perform a particular task. Oligonucleotides may be directlylabeled, used as primers in PCR or sequencing reactions, or bounddirectly to a solid substrate as in oligonucleotide arrays.

In some aspects of the invention, the probe may be affixed to a solidsubstrate. In other aspects of the invention, the sample may be affixedto a solid substrate. A probe or sample may be covalently bound to thesubstrate or it may be bound by some non covalent interaction includingelectrostatic, hydrophobic, hydrogen bonding, Van Der Waals, magnetic,or any other interaction by which a probe such as an oligonucleotideprobe may be attached to a substrate while maintaining its ability torecognize the allele to which it has specificity. A substrate may be anysolid or semi solid material onto which a probe may be affixed, attachedor printed, either singly or in the formation of a microarray. Examplesof substrate materials include but are not limited to polyvinyl,polysterene, polypropylene, polyester or any other plastic, glass,silicon dioxide or other silanes, hydrogels, gold, platinum, microbeads,micelles and other lipid formations, nitrocellulose, or nylon membranes.The substrate may take any form, including a spherical bead or flatsurface. For example, the probe may be bound to a substrate in the caseof an array. The sample may be bound to a substrate as (for example) thecase of a Southern Blot, Northern blot or other method that affixes thesample to a substrate.

Kits may also contain reagents that detect proteins, often through theuse of an antibody. These kits will contain one or more specificantibodies, buffers, and other reagents configured to detect binding ofthe antibody to the specific epitope. One or more of the antibodies maybe labeled with a fluorescent, enzymatic, magnetic, metallic, chemical,or other label that signifies and/or locates the presence ofspecifically bound antibody. The kit may also contain one or moresecondary antibodies that specifically recognize epitopes on otherantibodies. These secondary antibodies may also be labeled. The conceptof a secondary antibody also encompasses non-antibody ligands thatspecifically bind an epitope or label of another antibody. For example,streptavidin or avidin may bind to biotin conjugated to anotherantibody. Such a kit may also contain enzymatic substrates that changecolor or some other property in the presence of an enzyme that isconjugated to one or more antibodies included in the kit.

A kit may also contain an indication of a level of expression thatsignifies a particular physiological or cellular characteristic. Anindication includes any guide to a level of expression that, using thekit in which the indication is provided, would signal the presence orabsence of any physiological or cellular state that the kit isconfigured to detect. The indication may be expressed numerically,expressed as a color, expressed as an intensity of a band, derived froma standard curve, or derived from a control. The indication may be inthe form of a printed result that may be included in the kit or it maybe posted on a website or embedded in a software package.

Elements and acts in the example are intended to illustrate theinvention for the sake of simplicity and have not necessarily beenrendered according to any particular sequence or embodiment. The exampleis also intended to establish possession of the invention by theInventors.

EXAMPLES Example 1 Identification of Genes Expressed in the G2/M Phaseas Potential Biomarkers of Outcome in ACC

Previous gene expression studies have focused on identifying geneexpression signatures that differentiate ACC from benign adrenaladenomas and normal adrenal tissue (See References 5, 6, 8, 9, 15, and16). IGF2, FGFR1, FGFR2, FGFR4, and TOP2A have been shown to have agreater degree of expression in ACC while CDKN1C, KCNQ1, ADH1, IGFBP6,IGFR1, and ABCB1 show decreased expression in ACC (See References 9, 15,and 18). A panel of genes related to IGF2 signaling and steroidogenesisis almost as good as the Weiss score at predicting recurrence (SeeReference 5) and two additional studies have shown that ACC can besubdivided into two groups with differences in survival based on geneexpression profiles. (See References 6 and 8). A two-gene signature,PINK1 and BUB1B has been shown to be associated with poor prognosis (SeeReference 6).

Expression profiling was performed on 20 ACC tumors on two complementaryplatforms: the Affymetrix U133 Plus 2 and the Agilent 22K Human Genomemicroarray. Analysis of data from both platforms identifiedubiquitination and cell-cycle progression through G2/M as pathways thatare significantly dysregulated in ACC. Furthermore, several genesinvolved in the dysregulation of the G2/M transition are expressed in asimilar pattern as CDC2 (expressed when CDC2 expression is alsoexpressed.) Hierarchical clustering using sets of genes that wereexpressed in a similar or disparate (expressed when CDC2 expression issuppressed) pattern as CDC2 allowed grouping of ACC tumors byhistological grade. Perturbations of the IGF2-IGF1R signaling pathwaybeyond expression of IGF2 are an early event present in low-gradetumors, as are perturbations of the p53 pathway. Dysregulation of E2Ffamily members, particularly E2F1 and E2F4, were also observed, evenwhen the expression of cell cycle related genes were taken into account.Further, over-expression of PTTG1, a gene that links the G2/M transitionand p53 pathway was shown to be a biomarker of poor survival.

Clinical Samples

A total set of 30 ACC flash frozen tumors and 4 normal adrenal glandswere collected at the Mayo Clinic in Rochester, Minn., the UniversityHospital Essen (Essen, Germany) and the University of Calgary (Alberta,Canada). Research consent was obtained under the WIRB approved protocol#20051769. Tumor histological grade was determined using the Weisscriteria of adrenocortical carcinomas. (See Reference 17).

Expression Analysis

RNA was extracted from 100 mg samples of 30 ACC tumors and 4 normaladrenal tissues, amplified and reverse-transcribed with biotin labelednucleotides. Biotin-labeled cRNA was synthesized and purified using cRNAfilter cartridges. Labeled cRNA was fragmented and hybridized toAffymetrix U133 Plus 2 human genome arrays following the manufacturer'sprotocol. Scanning and washing was completed on a Fluidic Stations FS450and a GeneChip® Scanner 3000 with Workstation.

RNA from 15 of the above 30 tumors and normal total adrenal glands wasreverse transcribed, amplified and labeled with a fluorescent nucleotide(Cy5 for tumors and Cy3 for normal adrenals). The labeled cRNAs werethen co-hybridized to Agilent 22K human genome chips followingmanufacturer's protocols. The arrays were scanned, and the resulting TIFfiles were subject to feature extraction using the Agilent FE8.1.

Statistical Analysis

Affymetrix data quality was assessed using the affyQCReport package inBioconductor. Agilent data quality was accessed using the arrayQualitysoftware package. Nineteen of the Affymetrix chips passed qualitycontrol, and all Agilent chips passed, leading to a total of 20 tumorswith data, 14 of which have data from both platforms. The Affymetrixdata was normalized by RMA. Agilent data was processed similarly usingthe mArray and Limma software packages. Controls were removed from theAgilent dataset and minimum background subtracted. This data was thennormalized within arrays using the median values across the probes andnormalized across arrays using quantile normalization. In both thedatasets differentially expressed genes were identified using a linearmodel with fixed effects. A non-intercept model was used to determinethe differences between tumor and normal samples. Positive B-Statisticswas used to distinguish the set of differentially expressed genes.

RT-PCR Validation

Total RNA was reverse transcribed utilizing random hexamer primers and acDNA synthesis kit. The resulting cDNA was amplified with SYBR green andgene specific primers designed to span the closest intron-exon junctionof the reference sequence to which the probes on the array were designed(See Table 1). The cDNA was amplified using a 50° C. preheat step for 2min., a 95° C. heat activation step for 2 min., followed by 40 cycles ofdenaturation at 95° C. for 15 sec., annealing at the temperature inTable 1 for 30 sec., and elongation at 72° C. for 30 seconds. Meltingcurve analysis was performed to evaluate primer set specificity.Beta-actin was used as the reference gene. Fold difference in cDNAconcentration was calculated using the Pfaffl method taking into accountreaction efficiencies (See Reference 14).

Pathway Analysis

Pathway analysis identified cellular pathways with significantover-representation within the ACC versus normal tissue dataset.Affymetrix and Agilent data sets were each analyzed using both theInteractome Analysis in GeneGo and by Gene Set Enrichment Analysis usingcurated gene sets corresponding to Gene Ontology categories. Data setsincluded differentially expressed genes, genes showing a 2 fold orgreater change in expression or other expression changes as detailed inthe results.

Transcriptional Regulation Analysis

Cell cycle genes showing correlated expression (r≧|0.7|) with CDC2 wereexamined for common transcriptional elements in their promoters. Thesecell cycle associated genes were assessed for consensus promoter regionswithin the genomic sequence. Common transcriptional elements werecataloged. Additionally, the promoter sequences of the correlated genesup to 1000 bp up-stream of the transcriptional start site were parsedwith a natural language algorithm. The most common fragment size wasthen analyzed for sequence content and compared to the sequence of knowntranscription factor binding sites. Finally, the Interactome analysis ofGeneGo was used to identify transcription factors showingover-representation for both the Affymetrix and Agilent data consideredtogether for differentially expressed genes, genes showing a 2-fold orgreater change in expression, and genes differentially expressed or witha 2-fold or greater change in expression stratified by tumor grade. Forexample, low-0 grade tumors (grades 1 and 2) may be analyzedindependently from high-grade tumors (grades 3 and 4).

Flow Cytometry

Nuclei were isolated from frozen tissue and sorted on the basis of DAPIfluorescence for DNA content as previously described. (See Reference13.)

Analysis of the data from both the Affymetrix U133 Plus 2 platform andthe Agilent 22K microarray platform identified several genes thatdisplay differential expression when expression of the genes in ACC iscompared to non-diseased adrenal tissue. Differentially expressed genesinclude genes involved in IGF2 signaling, including IGF1R, IGFBP5, andIGFBP6, and TOP2A. IGF2 is over-expressed relative to normal adrenal byan average of 2.4 fold in the Affymetrix data and 1.71 fold in theAgilent data. H19 and CDKN1C (p57^(kip)) were found to be differentiallyunder-expressed in ACC relative to normal adrenal. RT-qPCR validation oftwo normal adrenal samples, three low-grade ACC and three high-grade ACCwas performed for a selection of genes including IGF2 (Table 1 forprimers, Table 3 for results). Analysis of IGF2 confirmedover-expression, in high-grade tumors showing greater increases inexpression.

Hierarchical clustering of genes shown to be differentially expressed inboth platforms allowed differentiation of normal from tumor andadditionally allowed differentiation of tumors of grades 1 and 2 fromtumors of grades 3 and 4. (Table 2) Independent gene ontology enrichmentanalysis on both the Affymetrix and Agilent data sets showed thatdifferentially expressed genes fell into cell cycle control andubiquitination pathways. Interactome analysis was used to find furtherdifferences between ACC and normal adrenals. When the interactome ofgenes identified as differentially expressed by both platforms wasanalyzed, there was a connection to the E2F transcription factors,particularly E2F1 and E2F4, as well as p53. Kinases that arose from theinteractome analysis were primarily centered on cell cycle control, raf,and ERK. There was also evidence of perturbations in chromatinremodeling because both p300 and HDAC1 were identified as over-connected(Table 3).

Gene Set Enrichment Analysis (GSEA) on the Affymetrix-derived data setusing curated gene sets relating to Gene Ontology categories yields arank ordering of the genes between classes. Upon inspection of the top50 ranked genes differentially expressed in normal adrenal and the top50 ranked genes differentially expressed in ACC, the genes with thegreatest degree of differential expression in normal adrenal relative toACC are all under-expressed in ACC. This underexpression is independentof tumor grade. The genes with the greatest degree of overexpression inACC relative to normal adrenal are differentially expressed with regardto tumor grade—specifically, high-grade tumors (pathological grade 3 andgrade 4) may be distinguished from low-grade (grades 1 and 2). GSEA fornormal adrenal as compared to ACC yielded no significant enrichment inthe normal samples. GSEA for ACC demonstrated enrichment in thechromosome organization and biogenesis set. Leading edge analysisdemonstrated that the enrichment in ACC was being driven by TP53, MDM2,and RAD51 expression (Table 2). Differential expression in normaladrenal was driven by CCL2, ADORA2A, and CXCL1. Many of the genes thatare overexpressed in ACC are part of the chromosome instabilityexpression cassette (See Reference 3). As a result, these genes werefiltered out of the analysis. A re-run of the GSEA analysis after thisfiltering increased the gene sets for ACC as a whole with onlychromosome organization and biogenesis remaining the same (Table 3).TP53 and MDM2 continued to drive the enrichment in gene sets regardlessof tumor grade.

Analyzing a list of genes from GO category enrichment refined the cellcycle enrichment category to the G2/M transition and mitosissubcategory. GeneGo interactome analysis highlighted the involvement ofthe E2F transcription factors, p53, c-myc, FOXM1, and SP1 among others.Over-connected kinases, phosphatases, enzymes, and other proteins werecentered on the G2/M and S-phase transitions of the cell cycle, DNArepair, and JNK signaling.

The genes used in the GeneGo analyses were expanded to include thosethat may not be differentially expressed but exhibited a ≧2-fold changein expression in either direction as compared to normal adrenals. GeneGointeractome analysis by tumor grade revealed that very little wasover-connected when looking at genes that were over-expressed 2 fold orgreater in low grade tumors but several proteins were over-connectedamong when looking at genes with a 2 fold or greater reduction inexpression. Both IGF1 and NOV, ligands for IGF1R, were identified andare also under expressed as was IGFBP5, a negative regulator of IGF2function. Additionally, there was evidence of dysregulation of TGF-βsignaling and extra-cellular matrix modulations. These results held whenthe CIN70 genes were removed. High grade tumors had similar results forunder-expressed genes. However, when looking at over-expressed genes,there were considerably more genes showing over-connection. Ofparticular note was the inclusion of not only proteins involved in cellcycle control, but DNA repair.

The identification of the cell cycle in general and G2/M in particularas biological processes enriched both the normal adrenal to ACCcomparison and the comparisons based on tumor grade led to a focus onCDC2 expression—specifically, genes that did not meet the criteria setfor differential expression but were overexpressed when CDC2 wasoverexpressed or underexpressed when CDC2 was underexpressed orunderexpressed when CDC2 was overexpressed in ACC relative to normal. Acorrelation analysis demonstrated both positive and negativecorrelations of CDC2 to other genes (Table 4). The genes resulting fromthis analysis were also able to differentiate tumors by grade. Since thedifferentiation correlates with grade and since grade correlates withsurvival, the expression of the genes included in the CDC2 correlatedgene set was assessed to determine association with survival. PTTG1 wasthe sole gene that allowed differentiation of tumors on the basis ofsurvival. Increased expression of this gene relative to a normalcontrols corresponded to decreased survival (FIGS. 1 and 2).

Example 2 PTTG1 is a Biomarker of Decreased Survival in ACC

PTTG1 encodes the protein securin. Securin is involved in the G2/Mtransition of the cell cycle by inhibiting the activity of separasethrough phosophorylation. It is degraded by the anaphase-promotingcomplex, releasing the inhibition of separase and promoting sisterchromatid separation. Securin has also been implicated in the negativeregulation of p53 through phosphorylation of p53.

PTTG1 was identified as a target of interest through expressionprofiling of 20 ACC tumor samples. PTTG1 was identified asdifferentially over-expressed 3-5 fold on both the Affymetrix U133 Plus2 and the Agilent Human 1A Oligo Microarray (v2) platforms. Analysis ofthat data identified the G2/M transition, particularly sister chromatidseparation, as being dysregulated. One such gene that showed coordinateexpression with CDC2 was PTTG1. PTTG1 was the sole gene tested to show asignificant association between the level of expression and survival(FIG. 1). A level of expression of 8.5, the median value of expressiondata from the Affymetrix platform over 19 samples, was used to segregatepatients into cohorts. The cohort of patients with a PTTG1 expressionvalue over 8.5 showed significantly poorer survival than the cohort ofpatients with an expression value less than 8.5 (FIG. 2).

The promoter sequences of the genes correlated with CDC2 expression wereanalyzed up to 1000 bp up-stream of the transcriptional start site usinga natural language algorithm. The most common fragment size was thenanalyzed for sequence content and compared to the sequence of knowntranscription factor binding sites. This analysis yielded the bindingsite for E2F1-Dp1. Using a complementary approach, binding site matricesof the promoters from the correlated genes were analyzed for commontranscription factor binding sites. Again, the analysis identifiedconsensus binding site for E2F1.

Example 3 PTTG1 Expression Correlates with a Cluster of ACC TumorsLikely to have Poor Survival

Examination of data from Giordano T J et al, Clin Cancer Res 15, 668-676(2009) showed that expression of PTTG1 did not significantly correlatewith survival and therefore teaches away from this invention (FIG. 3).Giordano does report the presence of ACC tumor clusters. FIG. 4 showsthat tumors classified as Cluster 1 have a poor survival outcome. Tumorsclassified as Cluster 2 have a better survival outcome. While PTTG1expression does not directly correlate with survival, PTTG1 doesdirectly correlate with cluster expression. FIG. 5 shows that tumorcluster 1 has higher PTTG1 expression than tumor cluster 2.

Example 4 Vorinostat Decreases PTTG1 Expression

Small molecule inhibitors that directly inhibit PTTG1 are unavailable.Vorinostat is a selective inhibitor of HDAC1, HDAC2, HDAC3, and HDAC6.FIGS. 6 and 7 show that vorinostat reduces PTTG1 expression in both theSW-13 and H259R ACC cell lines. In the figures, expression of PTTG1 isshown relative to the expression of beta-actin. Vorinostat reduces SW-13cell viability by 50% at a 1.7 μM concentration. It also reduces H295Rviability by 50% at a 0.9 LM.

REFERENCES

So as to reduce the complexity and length of the Detailed Specification,Inventor(s) herein expressly incorporate(s) by reference to the extentallowed all of the following materials.

-   1. Barlaskar F M et al, J Clin Endocrinol Metab 94, 204-212 (2009).-   2. Blake J A et al, Nucleic Acids Res 37, D712-D719 (2009).-   3. Carter S L et al, Nat Genet 38, 1043-1048 (2006).-   4. Cohn K et al, Surgery 100, 1170-1177 (1986).-   5. de Fraipont F et al, J Clin Endocrinol Metab 90, 1819-1829    (2005).-   6. de Reynies A et al, J Clin Oncol 27, 1108-1115 (2009).-   7. Decker R A et al, Surgery 110, 1006-1013 (1991).-   8. Giordano T J et al, Clin Cancer Res 15, 668-676 (2009).-   9. Giordano T J et al, Am J Pathol 162, 521-531 (2003).-   10. Ignaszak-Szczepaniak M et al, Oncol Rep 16 65-71 (2006).-   11. Laurell C et al, Eur J Endocrinol 161, 141-152 (2009).-   12. Lee J E et al, Surgery 118, 1090-1098 (1995).-   13. Paulson T G, Genome Res 9, 482-491 (1999).-   14. Pfaffl M W Nucleic Acids Res 29, e45 (2001).-   15. Slater E P, Eur J Endocrinol 154, 587-598 (2006).-   16. Velazquez-Fernandez D, Surgery 138, 1087-1094, (2005).-   17. Weiss L M, Am J Surg Pathol 8, 163-169 (1984).-   18. West A N et al, Cancer Res 67, 600-608 (2007).-   19. R Development Core Team (2008). R: A language and environment    for statistical computing. R Foundation for Statistical Computing,    Vienna, Austria. ISBN 3-900051-07-0, URL:http://www.R-project.org.-   20. Gentleman R C, et al. Genome Biol 5, R80 (2004

Tables

TABLE 1  PCR Primers Annealing  Biomarker Direction Sequence TempSEQ ID NO E2F1 Forward TCGTCTCCGAGGACACTGACAGCCA  59 SEQ ID NO. 8 E2F1Reverse GGAGGGGCTTTGATCACCATAACCA  59 SEQ ID NO. 9 SPARC ForwardTCTGACTGAGAAGCAGAAGCTGCGG  62 SEQ ID NO. 10 SPARC ReverseCCGAACTGCCAGTGTACAGGGAAGA  62 SEQ ID NO. 11 CDC2 ForwardCAGGAAGCCTAGCATCCCATGTC 59 SEQ ID NO. 12 CDC2 ReverseCCAGAAATTCGTTTGGCTGGATC 59 SEQ ID NO. 13 BIRC5 ForwardCCCTTGGTGAATTTTTGAAACTGGA  59 SEQ ID NO. 14 BIRC5 ReverseGCACTTTCTCCGCAGTTTCCTCAAA  59 SEQ ID NO. 15 TOP2A ForwardTCCTCCCCTCTGAATTTAGTTTGGG  62 SEQ ID NO. 16 TOP2A ReverseAAACAATGCCCATGAGATGGTCACT  62 SEQ ID NO. 17 IGF2 ForwardCCAAGTCCGAGAGGGACGTGTCGA 59 SEQ ID NO. 18 IGF2 ReverseTGGAAGAACTTGCCCACGGGG 59 SEQ ID NO. 19 IGF1R ForwardCCAACGAGCAAGTCCTTCGCTTCG 59 SEQ ID NO. 20 IGF1R ReverseGGGTTATACTGCCAGCACATGCGC 59 SEQ ID NO. 21

TABLE 2 Expression relative to normal adrenal of selected genes in ACCsamples. Samples are sorted according to grade.

TABLE 3 Legend

TABLE 3 Enriched Gene Sets from GSEA Analyses Under Various ConditionsComparison With CIN70 Signature Without CIN70 Signature Enriched GeneLeading Enriched Gene Sets Edge Sets Leading Edge Normal Adrenal: ACCNormal None CCL2, None CXCL1, CCL2, ADORA2A, FPR1, CXCL12, CXCL1,ADORA2A, ADRA2A, S100B ACC Chromosome TP53, MDM2, Chromatin TP53, TP63,MDM2 organization and RAD51, Binding, JNK biogenesis Cascade, Chromosomeorganization and biogenesis, Damaged DNA binding Normal Adrenal to lowgrade ACC Normal DNA packaging CCL2, CXCL1, none CCL2, CXCL1, (driven byIL8, S100B, IL8, CXCL12, HAT1) INHA S100B, INHA Low Grade ACC none TP53,MDM2, none TP53, TNIK, OXA1L, TNIK, RPS6KA5, MDM2 FOSB, NSD1, RPS6KA5,SMARCD3, KIF22, NCOA5, RAD51, TTK, BLM, MCM2 Normal Adrenal to HighGrade ACC Normal none CCL2, none CCL2, ADORA2A, ADORA2A, FPR1, CXCL12,CXCL1 ADRA2A, CXCL1, CCL11 High Grade ACC Nuclear Pore, TP53, MDMD2,Thyroid TP53, PPARBP, Chromosome NCOA5, RAD51, hormone MDM2, CRSP2,organization ILF3 receptor THRAP1 and binding, biogenesis, ChromosomeChromatim organization binding and biogenesis, Nucleoplasm, RNAsplicing, Splicosome complex

TABLE 4 Genes showing coordinated expression with CDC2 Table 4: Genesshowing Coordinated Expression with CDC2 Affymetrix Agilent Gene SymbolNM_014736 NM_014736 KIAA0101 NM_014736 NM_014736 CSNK1G1 AF213040NM_005192 CDKN3 AF213033 NM_005192 CDKN3 NM_002497 NM_002497 NEK2BC028974 NM_173480 ZNF57 NM_006101 NM_006101 NDC80 NM_018123 NM_018136ASPM NM_016448 NM_016448 DTL AK001261 NM_016448 DTL AK025578 NM_013282UHRF1 AU153848 NM_013277 RACGAP1 NM_022346 NM_022346 NCAPG NM_018492NM_018492 PBK NM_018131 NM_018131 CEP55 AU159942 NM_001067 TOP2AAL561834 NM_001067 TOP2A NM_004219 NM_004219 PTTG1 AF326731 NM_145697NUF2 NM_004701 NM_004701 CCNB2 AF394735 NM_002358 MAD2L1 NM_014791NM_014791 MELK NM_007057 NM_032997 ZWINT BE407516 NM_031966 CCNB1 N90191NM_031966 CCNB1 NM_005196 NM_016343 CENPF AF043294 NM_004336 BUB1NM_014750 NM_014750 DLG7 AL524035 NM_001786 CDC2 NM_001786 NM_001786CDC2 D88357 NM_001786 CDC2 NM_001168 NM_001168 BIRC5 NM_018685 NM_018685ANLN AK023208 NM_018685 ANLN NM_024629 NM_024629 MLF1IP AB032931NM_014176 UBE2T

1. A method of classifying adrenocortical carcinoma in a human patient,the method comprising: obtaining a sample of an adrenocortical carcinomatumor from a human patient; adding a first oligonucleotide and a secondoligonucleotide to a mixture comprising the sample of the adrenocorticalcarcinoma tumor, wherein the first and second oligonucleotides arecapable of binding to a nucleic acid biomarker comprising SEQ ID NO:1;subjecting the mixture to conditions that allow detection of the bindingof the first and second oligonucleotides to the biomarker; calculatingthe expression of the biomarker through the level of the binding of thefirst and second oligonucleotides to the biomarker, wherein theexpression of the biomarker in the sample is calculated in comparison tothe expression of the biomarker in a control sample; and classifying thehuman patient into one of a first cohort of individuals and a secondcohort of individuals, wherein individuals in the first cohort are notlikely to survive for a greater period of time after diagnosis withadrenocortical carcinoma compared to individuals in the second cohort,and further wherein the human patient is classified into the firstcohort of individuals based upon a determination of an at least threefold increase in expression of SEQ ID NO: 1 in the sample of theadrenocortical carcinoma tumor.
 2. The method of claim 1, wherein thefirst oligonucleotide and the second oligonucleotide are capable ofbinding to different nucleic acid strands.
 3. The method of claim 2 andfurther comprising adding a third oligonucleotide to the mixture whereinthe third oligonucleotide is capable of binding to a sequence betweenthe sequences to which the first oligonucleotide and the secondoligonucleotide are capable of binding.
 4. The method of claim 3,wherein the third oligonucleotide comprises a fluorescent label.
 5. Themethod of claim 3, wherein the third oligonucleotide comprises aquencher.
 6. The method of claim 2, wherein the nucleic acidamplification is verified by a method selected from the group consistingof a specifically sized band visualized through gel electrophoresis, aCt value, and DNA sequencing on a product of amplification.
 7. Themethod of claim 1, wherein the first and the second oligonucleotides areaffixed to a substrate.
 8. The method of claim 7, wherein the first andsecond oligonucleotides are configured to form a microarray.
 9. Themethod of claim 1 and further comprising first diagnosing the humanpatient as having an adrenocortical carcinoma tumor, wherein diagnosingthe human patient comprises obtaining some or all of the adrenocorticalcarcinoma tumor and evaluating the tumor for the presence ofadrenocortical carcinoma.
 10. The method of claim 1, wherein the humanpatient is classified into the first cohort of individuals based upon adetermination of a three to five fold increase in expression of thebiomarker in the sample of the adrenocortical carcinoma tumor
 11. Amethod of classifying adrenocortical carcinoma in a human patient, themethod comprising: obtaining a sample of an adrenocortical carcinomatumor from a human patient; adding a first antibody that is capable ofbinding to a biomarker comprising SEQ ID NO: 2 to a mixture comprisingthe sample of the adrenocortical carcinoma tumor from the human patient;subjecting the mixture to conditions that allow detection of the bindingof the first antibody to the biomarker; calculating the expression ofthe biomarker through the level of the binding of the first antibody tothe biomarker, wherein the expression of the biomarker in the sample iscalculated in comparison to the expression of the biomarker in a controlsample; and classifying the human patient into one of a first cohort ofindividuals and a second cohort of individuals, wherein individuals inthe first cohort are not likely to survive for a greater period of timeafter diagnosis with adrenocortical carcinoma compared to individuals inthe second cohort, and further wherein the human patient is classifiedinto the first cohort of individuals based upon a determination of an atleast three fold increase in expression of the biomarker in the sampleof the adrenocortical carcinoma tumor.
 12. The method of claim 11,wherein the first antibody comprises a label.
 13. The method of claim12, wherein the label is selected from a grouping consisting of afluorescent compound, an enzyme, and a ligand.
 14. The method of claim11, and further comprising adding a second antibody to the mixture,wherein the second antibody binds to the first antibody.
 15. The methodof claim 11 and further comprising first diagnosing the human patient ashaving an adrenocortical carcinoma tumor, wherein diagnosing the humanpatient comprises obtaining some or all of the adrenocortical carcinomatumor and evaluating the tumor for the presence of adrenocorticalcarcinoma.
 16. The method of claim 11, wherein the human patient isclassified into the first cohort of individuals based upon adetermination of a three to five fold increase in expression of thebiomarker in the sample of the adrenocortical carcinoma tumor.
 17. Amethod of determining a likelihood of survival of a human patient withadrenocortical carcinoma, the method comprising the steps of: diagnosingthe human patient as having an adrenocortical carcinoma tumor, whereindiagnosing the human patient comprises obtaining at least a portion ofthe adrenocortical carcinoma tumor from the human patient and evaluatingthe tumor for the presence of adrenocortical carcinoma; adding a firstoligonucleotide, a second oligonucleotide, and third oligonucleotide toa mixture comprising a sample of the adrenocortical carcinoma tumor inthe human patient, wherein the first, second, and third oligonucleotidesare capable of binding to a nucleotide acid biomarker comprising SEQ IDNO: 1; subjecting the mixture to conditions that allow detection of thebinding of the first, second, and third oligonucleotides to thebiomarker; and classifying the human patient as having a decreasedlikelihood for survival when there is an at least three fold increase inexpression of the biomarker in the sample as compared to a control. 18.The method of claim 17, wherein the human patient is classified ashaving a decreased likelihood for survival wherein there is a three tofive fold increase in expression of the biomarker in the sample comparedto the control.
 19. The method of claim 17, wherein the thirdoligonucleotide comprises a fluorescent label.
 20. The method of claim19, wherein the third oligonucleotide comprises a quencher.